The distance score and the similarity score capture inductive biases in the latent-space differently and their product functions as an `AND' between the two. Empirically, these scores taken individually are insufficient to estimate mistrust (Section REF ).
We note that TRUST-LAPSE will accept any \(\textit {d}\)  and \(\textit {sim}\)  that is most suited for the target domain.
We empirically choose the distance metric \(\textit {d}\)  to be the Mahalanobis distance (amongst distances like Euclidean, Manhattan, etc), obtained using class-conditional Gaussians [1]} without label-smoothing [2]} where class-wise means \(\mu _c\)  and covariance matrices \(\Sigma _c\)  are estimated from the coreset.
\( \textit {d}(\textbf {h}(x)) = \min _{c} \; (\textbf {h}(x) - \mu _c)^T\, \Sigma _c^{-1}\, (\textbf {h}(x) - \mu _c) \) 
